Consider the following linear programming problem:Max     Z = 11 x1 + 11 x2s.t.       20 x1 + 10 x2 ≤ 60       (constraint I)            x1 + 2 x2 ≤ 6     (constraint II)            x1, x2 ≥ 0The constraint lines and optimal solution are illustrated in the following figure:What is the lower bound of the X2 coefficient (C2) in the objective function, that doesn't change the optimal solution?(Round to one decimal place.)

To find the lower bound of the X2 coefficient (C2) in the objective function that doesn't change the optimal solution, we need to perform a sensitivity analysis.

Step 1: Identify the optimal solution from the graph. The optimal solution is the point where the two constraint lines intersect that yields the maximum value for the objective function.

Step 2: Identify the constraints that are binding at the optimal solution. These are the constraints that, if changed, would change the optimal solution.

Step 3: Calculate the slope of the objective function at the optimal solution. The slope of the objective function is -C1/C2, where C1 and C2 are the coefficients of x1 and x2 in the objective function.

Step 4: Calculate the slope of the binding constraints at the optimal solution. The slope of a constraint is -A1/A2, where A1 and A2 are the coefficients of x1 and x2 in the constraint.

Step 5: Compare the slope of the objective function to the slopes of the binding constraints. The lower bound of C2 is the value that makes the slope of the objective function equal to the slope of the steepest binding constraint.

Without the specific graph or the values of the optimal solution, we cannot provide a numerical answer. However, this is the process you would follow to find the lower bound of C2.